The CSL@BS research group covers a wide range in the field of computer science. A common theme for all research in the group is algorithm development and implementation. With a strong foundation in natural science and theoretical computer science, our main method consists of experimental evaluation of solutions, often coupled with proofs or statistical analysis.
The group endeavours to have strong participation in the international research community, with publications mainly in international conferences and journals. In the group we conduct both theoretical and applied research, often in close cooperation with industry.
There are two main focuses: parallel and distributed computing and data mining and machine learning.
The parallel and distributed computing research aims on developing practical solutions for efficient program development on today´s and future computing platforms. With a pragmatic approach we develop general as well as application or environment centric solutions, where our involvement span the whole process from algorithm design, analysis, proof, experimentation and down to deployable implementations and frameworks.
Within the field of data mining and machine learning, the aim is to develop and improve machine learning methods, techniques and algorithms for use in data mining. Although we develop solutions that are general purpose rather than application specific, we continuously evaluate our solutions on real-world problems in collaboration with industrial partners.